Big Data shows big promise in medicine

In handling some life-or-death medical judgements, computers have already surpassed the abilities of doctors. We’re looking at the promise of self-driving cars, according to Zak Kohane, a doctor and researcher at Harvard Medical School. On the roads, replacing drivers with computers could save lives that would otherwise be lost to human error. In medicine, replacing intuition with machine intelligence might save patients from drug side effects or otherwise incurable cancers.

Consider precision medicine, which involves tailoring drugs to individual patients. And to understand its promise, look to Shirley Pepke, a physicist who migrated into computational biology. When she developed a deadly cancer, she responded like a scientist and fought it using Big Data. And she is winning. She shared her story at a recent conference organized by Kohane.

In 2013, Pepke was diagnosed with advanced ovarian cancer. She was 46, and her children were nine and three years old. It was just two months after her annual gynaecological exam. She had symptoms, which the doctors brushed off, until her bloating got so bad she insisted on an ultrasound. She was carrying six litres of fluid caused by the cancer, which had metastasized.

She did what most people do in her position. She agreed to a course of chemotherapy. She also did something most people wouldn’t know how to do—she started looking for useful data. After all, tumours are full of data. They carry DNA with various abnormalities, some of which make them malignant or resistant to certain drugs. Armed with that information, doctors design more effective, individualized treatments. Already, breast cancers are treated differently depending on whether they have a mutation in a gene called HER2. So far, scientists have found no such genetic divisions for ovarian cancers.

But there was some data. Years earlier, scientists had started a data bank called the Cancer Genome Atlas. There were genetic sequences on about 400 ovarian tumours. To help her extract information, she turned to Greg ver Steeg, a professor at the University of Southern California, who was working on an automated pattern-recognition technique called correlation explanation (CorEx). It had not been used to evaluate cancer, but she and Ver Steeg thought it might work. She also got genetic sequencing done on her tumour.

In the meantime, she found out she was not one of the lucky patients cured by chemotherapy. The cancer came back.

But CorEx had turned up a clue. Her tumour had something common with those of the luckier women who responded to the chemotherapy—an off-the-charts signal for an immune system product called cytokines.